Can Tools Reliably Tell Whether Code Was Written by AI or a Human?

In practice, it’s extremely difficult to distinguish AI-written code from human-written code based only on the final source. Unless the code includes some kind of watermark or there’s a record of how it was produced, there usually isn’t definitive proof. Patterns such as overly generic comments, repetitive structure, or common design choices might be clues, but they aren’t reliable evidence.

A linter isn’t designed to identify who wrote the code. It checks things like syntax, formatting, type errors, and common bugs. You could build a separate analysis tool that looks for statistical or stylistic patterns, but any reliable detector would probably be easy to evade by feeding its results back into the generation process and asking for more human-like output.

AI-generated code can sometimes favor familiar, easy-to-explain patterns because those patterns appear frequently in its training material. For example, it may repeatedly suggest a singleton or produce boilerplate-heavy solutions. That can be a useful hint during review, but humans use the same patterns too, so it cannot serve as a dependable identification method.